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        创建时间:<time class="date" title='更新时间: 2020-03-18 15:16:23'>2020-03-17 14:56</time>
        
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        <ol class="toc"><li class="toc-item toc-level-2"><a class="toc-link" href="#pytorch实现反向传播算法"><span class="toc-text"> Pytorch实现反向传播算法</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#使用pytorch搭建网络"><span class="toc-text"> 使用pytorch搭建网络</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#知识拓展"><span class="toc-text"> 知识拓展</span></a></li></ol>
    
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        <h2 id="pytorch实现反向传播算法"><a class="markdownIt-Anchor" href="#pytorch实现反向传播算法"></a> Pytorch实现反向传播算法</h2>
<ul>
<li>pytorch好处就是能使用GPU进行计算，加快运算时间</li>
<li>numpy只能使用CPU进行运算，效率没它高</li>
</ul>
<p><img src="http://q6ip4it64.bkt.clouddn.com/42D750D3-6703-4677-B25E-A5F05F6D0392.jpeg?e=1584451477&amp;token=IeqxMYJS9TcEnX8V6lUXD9FF_y3SCdOBApPAMpRy:nxMTLMUL7A10hrw0YcCed85sk5I=&amp;attname=" alt="" /></p>
<pre class="highlight"><code class="">import torch

# 创建一个张量并设置 requires_grad=True 用来追踪他的计算历史
x = torch.ones(2, 2, requires_grad=True)
print(x)
# tensor([[1., 1.],
#         [1., 1.]], requires_grad=True)


# 对张量进行计算操作，grad_fn已经被自动生成了。
y = x + 2
print(y)
# tensor([[3., 3.],
#         [3., 3.]], grad_fn=&lt;AddBackward&gt;)
print(y.grad_fn)
# &lt;AddBackward object at 0x00000232535FD860&gt;

# 对y进行一个乘法操作
z = y * y * 3
out = z.mean()

print(z)
# tensor([[27., 27.],
#         [27., 27.]], grad_fn=&lt;MulBackward&gt;)
print(out)
# tensor(27., grad_fn=&lt;MeanBackward1&gt;)
</code></pre>
<p><img src="http://q6ip4it64.bkt.clouddn.com/%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD.png?e=1584434692&amp;token=IeqxMYJS9TcEnX8V6lUXD9FF_y3SCdOBApPAMpRy:UUQl9OBBkFxPoOwGAa4Snz1sgWY=&amp;attname=" alt="" /></p>
<ul>
<li>也就是说，如果少了backward()，他就不会自动回带x的值，调用x.grad就会显示空</li>
<li>当设置<code>requird_grad=True</code>时，就会对该张量贴一个标签，自动追踪这个张量的所有运算，所以即使复合运算也没关系</li>
</ul>
<p><a href="%5Bhttps://www.cnblogs.com/LXP-Never/p/11616289.html#%E6%A2%AF%E5%BA%A6%5D(https://www.cnblogs.com/LXP-Never/p/11616289.html#%E6%A2%AF%E5%BA%A6)">参考博客</a></p>
<ul>
<li>当自变量只有1个的时候</li>
</ul>
<pre class="highlight"><code class="">import torch
from torch.autograd import Variable
import torch.nn as nn
import numpy as np

data = np.array(3, dtype=float)
x = torch.from_numpy(data)
x = Variable(x, requires_grad = True)
y = x**2
y.backward()
print(x.grad)
</code></pre>
<ul>
<li>当输入为多个的时候,先看看推导，始终有个偏置为1，他们共用一套系数</li>
</ul>
<p><img src="http://q6ip4it64.bkt.clouddn.com/A94138CB-7BFC-4154-AF1A-BF9E9D907B70.jpeg?e=1584435173&amp;token=IeqxMYJS9TcEnX8V6lUXD9FF_y3SCdOBApPAMpRy:-rl5WQMVH8pq_3SQ3C5fqYMTo4E=&amp;attname=" alt="" /></p>
<pre class="highlight"><code class="">import torch

data_x = [
    [1,2,1],
    [2,3,1]
]
target = [[2.1,3]]
data_w = [[3,4,5]]
stride = 0.01
x = torch.tensor(data_x).float()
y = torch.tensor(target).float()
w = torch.tensor(data_w).float()
w.requires_grad = True
count = 0
while True:
    count += 1
    out = torch.mm(x, w.t())
    temp = torch.sub(y, out.t())[0]
    loss_func = temp[0]**2 + temp[1]**2
    if loss_func.data &lt; 0.00001:
        break
    loss_func.backward()
    w.data -= w.grad*stride
    w.grad *= 0
print(out)
print(out.data)
</code></pre>
<pre class="highlight"><code class="">tensor([[2.1026],
        [2.9983]], grad_fn=&lt;MmBackward&gt;)
tensor([[2.1026],
        [2.9983]])
</code></pre>
<ul>
<li>注意一定要用矩阵表示<code>target = [[2,3]]</code>,不能用列表表示<code>target = [2,3]</code></li>
<li><code>Tensor(data_x)</code>，这样才能将矩阵转化成张量，在GPU中运算，转化后默认时浮点型</li>
<li><code>requires_grad=True</code>，开启自动求导模式后，就会对变量w进行追踪，不论w进行了什么运算都能标记</li>
<li><code>torch.mm，进行张量的乘法</code></li>
<li><code>w.t()，张量的转置</code></li>
<li><code>torch.sub，torch</code>中没有定义<code>__sub__</code>这个魔法方法，所以不能用<code>-</code>号操作</li>
<li><code>loss_func.backward()</code>，<strong>在反向传播时，只能对损失函数进行反向传播，我们前面定义了w的自动求导，因为x为输入是固定的，不需要求导，定义后就会对损失函数中的w求导，并带x的值进去</strong></li>
<li><code>w.grad *= 0 ##w.grad = torch.Tensor([[0,0,0]])</code>，这一步是必须的，torch中的自动求导运算，每次循环都会进行梯度累加，所以每次循环必须要清零,<a href="https://blog.csdn.net/yangwangnndd/article/details/94667922" target="_blank" rel="noopener">为什么请看这里</a></li>
</ul>
<pre class="highlight"><code class="">import numpy as np

a = np.array([[1,1,1]])
print(a.shape)
b = np.array([1,1,1])
print(b.shape)
</code></pre>
<pre class="highlight"><code class="">(1, 3)
(3,)
</code></pre>
<p><a href="https://www.cnblogs.com/kiwiwk/p/11711671.html" target="_blank" rel="noopener">如何用pytorch搭建简单的神经网络参考博客</a></p>
<h2 id="使用pytorch搭建网络"><a class="markdownIt-Anchor" href="#使用pytorch搭建网络"></a> 使用pytorch搭建网络</h2>
<ul>
<li>nn是neural network包，与神经网络有关</li>
</ul>
<pre class="highlight"><code class="">import torch as t
import torch.nn as nn

x = t.tensor([[1, 2], [3, 4]]).float()
y = t.tensor([[3], [7]]).float()

my_net = nn.Sequential(
    nn.Linear(2, 3),
    nn.Sigmoid(),
    nn.Linear(3, 7),
    nn.Sigmoid(),
    nn.Linear(7, 1)
)
optimizer = t.optim.Adam(my_net.parameters(), lr=0.05)
loss_func = nn.MSELoss()

while True:
    out = my_net(x)
    loss = loss_func(out, y)
    if loss.data &lt; 0.0001:
        break
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print(my_net(x).data)
</code></pre>
<ul>
<li><strong>问题1,当我想输入<code>x = t.tensor([[1, 2], [3, 4]]).float()</code>,<code>y = t.tensor([[3], [7]]).float()</code>，输入是2*2的，输出是2*1的，那么神经网络的输出，应该对应多少？</strong></li>
</ul>
<pre class="highlight"><code class="">    nn.Linear(2, 3),
    nn.Sigmoid(),
    nn.Linear(3, 7),
    nn.Sigmoid(),
    nn.Linear(7, 1)
</code></pre>
<p>我们可以看到这个网络有2个输入，1个输出，运行结果，输出为2行1列</p>
<pre class="highlight"><code class="">tensor([[3.0136],
        [7.0019]])
</code></pre>
<p>当我对输出进行修改时，此时会发出警告：</p>
<pre class="highlight"><code class="">    nn.Linear(2, 3),
    nn.Sigmoid(),
    nn.Linear(3, 7),
    nn.Sigmoid(),
    nn.Linear(7, 2)
</code></pre>
<pre class="highlight"><code class="">tensor([[3.0126, 2.9870],
        [6.9957, 7.0069]])
</code></pre>
<p>由上面可以自己得出结论</p>
<blockquote>
<p>对pytorch搭建网络的详细分析:</p>
<ul>
<li>
<p><code>x = t.tensor([[1, 2], [3, 4]]).float()</code>首先输入的数据一定要是浮点型</p>
</li>
<li>
<pre class="highlight"><code class=""></code></pre>
</li>
</ul>
<p>my_net = nn.Sequential(<br />
nn.Linear(2, 3),<br />
nn.Sigmoid(),<br />
nn.Linear(3, 7),<br />
nn.Sigmoid(),<br />
nn.Linear(7, 1)<br />
)<br />
创建神经网络，要记得输入和输出相连接，一般最后一层不需要激励函数<br />
<code>my_net</code>实例化后，就有个parameters方法，生成权重</p>
<pre class="highlight"><code class=""></code></pre>
<pre class="highlight"><code class="">
</code></pre>
<pre class="highlight"><code class="">
</code></pre>
<pre class="highlight"><code class=""></code></pre>
</blockquote>
<hr />
<blockquote>
<ul>
<li><code>optimizer = t.optim.Adam(my_net.parameters(), lr=0.05)</code>创建优化器实例，传入网络参数和学习率</li>
<li><code>loss_func = nn.MSELoss()</code>调用损失函数方法，这里使用的是平均损失函数</li>
<li><code>out = my_net(x)</code>，传入输入，<code>loss = loss_func(out, y)</code>，计算误差</li>
<li><code>optimizer.zero_grad()</code>将优化器中的梯度清0</li>
<li><code>loss.backward()</code>反向传播</li>
<li><code>optimizer.step()</code>参数更新</li>
</ul>
</blockquote>
<p>假如现在有新的数据，我们对其进行计算</p>
<ul>
<li>这是训练集，我们可以看到是在做加法运算，输入1，2，3，对应输出6，可以看出输入应该是3，输出应该是1</li>
</ul>
<pre class="highlight"><code class="">x = t.tensor([[1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9]]).float()
y = t.tensor([[6], [12], [18], [24]]).float()
</code></pre>
<p>这样即可</p>
<pre class="highlight"><code class="">x1 = t.tensor([[1, 3, 2]]).float()
print(my_net(x1).data)
</code></pre>
<h2 id="知识拓展"><a class="markdownIt-Anchor" href="#知识拓展"></a> 知识拓展</h2>
<p>如果想将神经网络放到GPU上跑</p>
<pre class="highlight"><code class="">比如现在定义了一个类
c = MyNet()
c.cuda()这样就行了
</code></pre>
<p>如果想将数据<code>x</code>放到<code>GPU</code>，可以<code>x.cuda()</code></p>
<p>要将数据从<code>GPU</code>中取回来，只用<code>x.cpu()</code>即可</p>

      
       
    </div>
</article>



<div class="article_copyright">
    <p><span class="copy-title">文章标题:</span>Pytorch 搭建神经网络</p>
    <p><span class="copy-title">文章字数:</span><span class="post-count">1.3k</span></p>
    <p><span class="copy-title">本文作者:</span><a  title="Miki Zhu">Miki Zhu</a></p>
    <p><span class="copy-title">发布时间:</span>2020-03-17, 14:56:16</p>
    <p><span class="copy-title">最后更新:</span>2020-03-18, 15:16:23</p>
    <span class="copy-title">原始链接:</span><a class="post-url" href="/2020/03/17/Pytorch-%E6%90%AD%E5%BB%BA%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/" title="Pytorch 搭建神经网络">http://mikiblog.online/2020/03/17/Pytorch-%E6%90%AD%E5%BB%BA%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</a>
    <p>
        <span class="copy-title">版权声明:</span><i class="fa fa-creative-commons"></i> <a rel="license noopener" href="http://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" title="CC BY-NC-SA 4.0 International" target = "_blank">"署名-非商用-相同方式共享 4.0"</a> 转载请保留原文链接及作者。
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